Partial information: converted files to UNIX EOL convention
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function [irfmat,irfst]=PCL_Part_info_irf( H, varobs, M_, dr, irfpers,ii)
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% sets up parameters and calls part-info kalman filter
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% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
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% suit partial information RE solution in accordance with, and based on, the
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% Pearlman, Currie and Levine 1986 solution.
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% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
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% Copyright (C) 2001-20010 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% Recall that the state space is given by the
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% predetermined variables s(t-1), x(t-1)
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% and the jump variables x(t).
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% The jump variables have dimension NETA
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[junk,OBS] = ismember(varobs,M_.endo_names,'rows');
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G1=dr.PI_ghx;
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impact=dr.PI_ghu;
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nmat=dr.PI_nmat;
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CC=dr.PI_CC;
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NX=M_.exo_nbr; % no of exogenous varexo shock variables.
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FL_RANK=dr.PI_FL_RANK;
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NY=M_.endo_nbr;
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if isempty(OBS)
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NOBS=NY;
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LL=eye(NY,NY);
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else %and if no obsevations specify OBS=[0] but this is not going to work properly
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NOBS=length(OBS);
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LL=zeros(NOBS,NY);
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for i=1:NOBS
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LL(i,OBS(i))=1;
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end
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end
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if exist( 'irfpers')==1
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if ~isempty(irfpers)
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if irfpers<=0, irfpers=20, end;
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else
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irfpers=20;
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end
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else
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irfpers=20;
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end
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ss=size(G1,1);
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pd=ss-size(nmat,1);
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SDX=M_.Sigma_e^0.5; % =SD,not V-COV, of Exog shocks or M_.Sigma_e^0.5 num_exog x num_exog matrix
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if isempty(H)
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H=M_.H;
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end
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VV=H; % V-COV of observation errors.
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MM=impact*SDX; % R*(Q^0.5) in standard KF notation
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% observation vector indices
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% mapping to endogenous variables.
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L1=LL*dr.PI_TT1;
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L2=LL*dr.PI_TT2;
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MM1=MM(1:ss-FL_RANK,:);
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U11=MM1*MM1';
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% SDX
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U22=0;
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% determine K1 and K2 observation mapping matrices
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% This uses the fact that measurements are given by L1*s(t)+L2*x(t)
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% and s(t) is expressed in the dynamics as
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% H1*eps(t)+G11*s(t-1)+G12*x(t-1)+G13*x(t).
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% Thus the observations o(t) can be written in the form
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% o(t)=K1*[eps(t)' s(t-1)' x(t-1)']' + K2*x(t) where
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% K1=[L1*H1 L1*G11 L1*G12] K2=L1*G13+L2
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G12=G1(NX+1:ss-2*FL_RANK,:);
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KK1=L1*G12;
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K1=KK1(:,1:ss-FL_RANK);
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K2=KK1(:,ss-FL_RANK+1:ss)+L2;
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%pre calculate time-invariant factors
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A11=G1(1:pd,1:pd);
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A22=G1(pd+1:end, pd+1:end);
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A12=G1(1:pd, pd+1:end);
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A21=G1(pd+1:end,1:pd);
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Lambda= nmat*A12+A22;
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%A11_A12Nmat= A11-A12*nmat % test
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I_L=inv(Lambda);
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BB=A12*inv(A22);
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FF=K2*inv(A22);
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QQ=BB*U22*BB' + U11;
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UFT=U22*FF';
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% kf_param structure:
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AA=A11-BB*A21;
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CCCC=A11-A12*nmat; % F in new notation
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DD=K1-FF*A21; % H in new notation
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EE=K1-K2*nmat;
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RR=FF*UFT+VV;
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if ~any(RR)
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% if zero add some dummy measurement err. variance-covariances
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% with diagonals 0.000001. This would not be needed if we used
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% the slow solver, or the generalised eigenvalue approach,
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% but these are both slower.
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RR=eye(size(RR,1))*1.0e-6;
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end
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SS=BB*UFT;
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VKLUFT=VV+K2*I_L*UFT;
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ALUFT=A12*I_L*UFT;
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FULKV=FF*U22*I_L'*K2'+VV;
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FUBT=FF*U22*BB';
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nmat=nmat;
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% initialise pshat
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AQDS=AA*QQ*DD'+SS;
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DQDR=DD*QQ*DD'+RR;
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I_DQDR=inv(DQDR);
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AQDQ=AQDS*I_DQDR;
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ff=AA-AQDQ*DD;
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hh=AA*QQ*AA'-AQDQ*AQDS';%*(DD*QQ*AA'+SS');
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rr=DD*QQ*DD'+RR;
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ZSIG0=disc_riccati_fast(ff,DD,rr,hh);
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PP=ZSIG0 +QQ;
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exo_names=M_.exo_names(M_.exo_names_orig_ord,:);
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DPDR=DD*PP*DD'+RR;
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I_DPDR=inv(DPDR);
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PDIDPDRD=PP*DD'*I_DPDR*DD;
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%GG=[CCCC (AA-CCCC)*(eye(ss-FL_RANK)-PP*DD'*I_DQDR*DD); zeros(ss-FL_RANK) AA*(eye(ss-FL_RANK)-PP*DD'*I_DQDR*DD)];
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GG=[CCCC (AA-CCCC)*(eye(ss-FL_RANK)-PDIDPDRD); zeros(ss-FL_RANK) AA*(eye(ss-FL_RANK)-PDIDPDRD)];
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imp=[impact(1:ss-FL_RANK,:); impact(1:ss-FL_RANK,:)];
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% Calculate IRFs of observable series
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%The extra term in leads to
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%LL0=[EE (H-EE)(I-PH^T(HPH^T+V)^{-1}H)].
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%Then in the case of observing all variables without noise (V=0), this
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% leads to LL0=[EE 0].
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I_PD=(eye(ss-FL_RANK)-PDIDPDRD);
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LL0=[ EE (DD-EE)*I_PD];
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%OVV = [ zeros( size(dr.PI_TT1,1), NX ) dr.PI_TT1 dr.PI_TT2];
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VV = [ dr.PI_TT1 dr.PI_TT2];
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stderr=diag(M_.Sigma_e^0.5);
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irfmat=zeros(size(dr.PI_TT1 ,1),irfpers);
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irfst=zeros(size(GG,1),irfpers);
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irfst(:,1)=stderr(ii)*imp(:,ii);
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for jj=2:irfpers;
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irfst(:,jj)=GG*irfst(:,jj-1); % xjj=f irfstjj-2
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irfmat(:,jj-1)=VV*irfst(NX+1:ss-FL_RANK,jj);
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%irfmat(:,jj)=LL0*irfst(:,jj);
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end
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save ([M_.fname '_PCL_PtInfoIRFs_' num2str(ii) '_' deblank(exo_names(ii,:))], 'irfmat','irfst');
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function [irfmat,irfst]=PCL_Part_info_irf( H, varobs, M_, dr, irfpers,ii)
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% sets up parameters and calls part-info kalman filter
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% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
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% suit partial information RE solution in accordance with, and based on, the
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% Pearlman, Currie and Levine 1986 solution.
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% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
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% Copyright (C) 2001-20010 Dynare Team
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%
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% This file is part of Dynare.
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%
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% Dynare is free software: you can redistribute it and/or modify
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% it under the terms of the GNU General Public License as published by
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% the Free Software Foundation, either version 3 of the License, or
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% (at your option) any later version.
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%
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% Dynare is distributed in the hope that it will be useful,
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% but WITHOUT ANY WARRANTY; without even the implied warranty of
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% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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% GNU General Public License for more details.
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%
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% You should have received a copy of the GNU General Public License
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% Recall that the state space is given by the
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% predetermined variables s(t-1), x(t-1)
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% and the jump variables x(t).
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% The jump variables have dimension NETA
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[junk,OBS] = ismember(varobs,M_.endo_names,'rows');
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G1=dr.PI_ghx;
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impact=dr.PI_ghu;
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nmat=dr.PI_nmat;
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CC=dr.PI_CC;
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NX=M_.exo_nbr; % no of exogenous varexo shock variables.
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FL_RANK=dr.PI_FL_RANK;
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NY=M_.endo_nbr;
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if isempty(OBS)
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NOBS=NY;
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LL=eye(NY,NY);
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else %and if no obsevations specify OBS=[0] but this is not going to work properly
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NOBS=length(OBS);
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LL=zeros(NOBS,NY);
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for i=1:NOBS
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LL(i,OBS(i))=1;
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end
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end
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if exist( 'irfpers')==1
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if ~isempty(irfpers)
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if irfpers<=0, irfpers=20, end;
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else
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irfpers=20;
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end
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else
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irfpers=20;
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end
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ss=size(G1,1);
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pd=ss-size(nmat,1);
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SDX=M_.Sigma_e^0.5; % =SD,not V-COV, of Exog shocks or M_.Sigma_e^0.5 num_exog x num_exog matrix
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if isempty(H)
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H=M_.H;
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end
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VV=H; % V-COV of observation errors.
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MM=impact*SDX; % R*(Q^0.5) in standard KF notation
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% observation vector indices
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% mapping to endogenous variables.
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L1=LL*dr.PI_TT1;
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L2=LL*dr.PI_TT2;
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MM1=MM(1:ss-FL_RANK,:);
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U11=MM1*MM1';
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% SDX
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U22=0;
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% determine K1 and K2 observation mapping matrices
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% This uses the fact that measurements are given by L1*s(t)+L2*x(t)
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% and s(t) is expressed in the dynamics as
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% H1*eps(t)+G11*s(t-1)+G12*x(t-1)+G13*x(t).
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% Thus the observations o(t) can be written in the form
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% o(t)=K1*[eps(t)' s(t-1)' x(t-1)']' + K2*x(t) where
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% K1=[L1*H1 L1*G11 L1*G12] K2=L1*G13+L2
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G12=G1(NX+1:ss-2*FL_RANK,:);
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KK1=L1*G12;
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K1=KK1(:,1:ss-FL_RANK);
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K2=KK1(:,ss-FL_RANK+1:ss)+L2;
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%pre calculate time-invariant factors
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A11=G1(1:pd,1:pd);
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A22=G1(pd+1:end, pd+1:end);
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A12=G1(1:pd, pd+1:end);
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A21=G1(pd+1:end,1:pd);
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Lambda= nmat*A12+A22;
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%A11_A12Nmat= A11-A12*nmat % test
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I_L=inv(Lambda);
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BB=A12*inv(A22);
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FF=K2*inv(A22);
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QQ=BB*U22*BB' + U11;
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UFT=U22*FF';
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% kf_param structure:
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AA=A11-BB*A21;
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CCCC=A11-A12*nmat; % F in new notation
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DD=K1-FF*A21; % H in new notation
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EE=K1-K2*nmat;
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RR=FF*UFT+VV;
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if ~any(RR)
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% if zero add some dummy measurement err. variance-covariances
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% with diagonals 0.000001. This would not be needed if we used
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% the slow solver, or the generalised eigenvalue approach,
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% but these are both slower.
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RR=eye(size(RR,1))*1.0e-6;
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end
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SS=BB*UFT;
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VKLUFT=VV+K2*I_L*UFT;
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ALUFT=A12*I_L*UFT;
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FULKV=FF*U22*I_L'*K2'+VV;
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FUBT=FF*U22*BB';
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nmat=nmat;
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% initialise pshat
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AQDS=AA*QQ*DD'+SS;
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DQDR=DD*QQ*DD'+RR;
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I_DQDR=inv(DQDR);
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AQDQ=AQDS*I_DQDR;
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ff=AA-AQDQ*DD;
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hh=AA*QQ*AA'-AQDQ*AQDS';%*(DD*QQ*AA'+SS');
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rr=DD*QQ*DD'+RR;
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ZSIG0=disc_riccati_fast(ff,DD,rr,hh);
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PP=ZSIG0 +QQ;
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exo_names=M_.exo_names(M_.exo_names_orig_ord,:);
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DPDR=DD*PP*DD'+RR;
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I_DPDR=inv(DPDR);
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PDIDPDRD=PP*DD'*I_DPDR*DD;
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%GG=[CCCC (AA-CCCC)*(eye(ss-FL_RANK)-PP*DD'*I_DQDR*DD); zeros(ss-FL_RANK) AA*(eye(ss-FL_RANK)-PP*DD'*I_DQDR*DD)];
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GG=[CCCC (AA-CCCC)*(eye(ss-FL_RANK)-PDIDPDRD); zeros(ss-FL_RANK) AA*(eye(ss-FL_RANK)-PDIDPDRD)];
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imp=[impact(1:ss-FL_RANK,:); impact(1:ss-FL_RANK,:)];
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% Calculate IRFs of observable series
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%The extra term in leads to
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%LL0=[EE (H-EE)(I-PH^T(HPH^T+V)^{-1}H)].
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%Then in the case of observing all variables without noise (V=0), this
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% leads to LL0=[EE 0].
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I_PD=(eye(ss-FL_RANK)-PDIDPDRD);
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LL0=[ EE (DD-EE)*I_PD];
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%OVV = [ zeros( size(dr.PI_TT1,1), NX ) dr.PI_TT1 dr.PI_TT2];
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VV = [ dr.PI_TT1 dr.PI_TT2];
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stderr=diag(M_.Sigma_e^0.5);
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irfmat=zeros(size(dr.PI_TT1 ,1),irfpers);
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irfst=zeros(size(GG,1),irfpers);
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irfst(:,1)=stderr(ii)*imp(:,ii);
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for jj=2:irfpers;
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irfst(:,jj)=GG*irfst(:,jj-1); % xjj=f irfstjj-2
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irfmat(:,jj-1)=VV*irfst(NX+1:ss-FL_RANK,jj);
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%irfmat(:,jj)=LL0*irfst(:,jj);
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end
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save ([M_.fname '_PCL_PtInfoIRFs_' num2str(ii) '_' deblank(exo_names(ii,:))], 'irfmat','irfst');
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@ -1,200 +1,200 @@
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function [irfmat,irfst]=PCL_Part_info_moments( H, varobs, dr,ivar)
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% sets up parameters and calls part-info kalman filter
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% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
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% suit partial information RE solution in accordance with, and based on, the
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% Pearlman, Currie and Levine 1986 solution.
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% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
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% Copyright (C) 2001-20010 Dynare Team
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%
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% This file is part of Dynare.
|
||||
%
|
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% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
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% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
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%
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% You should have received a copy of the GNU General Public License
|
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% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
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% Recall that the state space is given by the
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% predetermined variables s(t-1), x(t-1)
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% and the jump variables x(t).
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% The jump variables have dimension NETA
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global M_ options_ oo_
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warning_old_state = warning;
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warning off
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[junk,OBS] = ismember(varobs,M_.endo_names,'rows');
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G1=dr.PI_ghx;
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impact=dr.PI_ghu;
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nmat=dr.PI_nmat;
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CC=dr.PI_CC;
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NX=M_.exo_nbr; % no of exogenous varexo shock variables.
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% NETA=dr.nfwrd+dr.nboth; % total no of exp. errors set to no of forward looking equations
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FL_RANK=dr.PI_FL_RANK;
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NY=M_.endo_nbr;
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if isempty(OBS)
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NOBS=NY;
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LL=eye(NY,NY);
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else %and if no obsevations specify OBS=[0] but this is not going to work properly
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NOBS=length(OBS);
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LL=zeros(NOBS,NY);
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for i=1:NOBS
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LL(i,OBS(i))=1;
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end
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end
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if exist( 'irfpers')==1
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if ~isempty(irfpers)
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if irfpers<=0, irfpers=20, end;
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else
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irfpers=20;
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end
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else
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irfpers=20;
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end
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ss=size(G1,1);
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pd=ss-size(nmat,1);
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SDX=M_.Sigma_e^0.5; % =SD,not V-COV, of Exog shocks or M_.Sigma_e^0.5 num_exog x num_exog matrix
|
||||
if isempty(H)
|
||||
H=M_.H;
|
||||
end
|
||||
VV=H; % V-COV of observation errors.
|
||||
MM=impact*SDX; % R*(Q^0.5) in standard KF notation
|
||||
% observation vector indices
|
||||
% mapping to endogenous variables.
|
||||
|
||||
L1=LL*dr.PI_TT1;
|
||||
L2=LL*dr.PI_TT2;
|
||||
|
||||
MM1=MM(1:ss-FL_RANK,:);
|
||||
U11=MM1*MM1';
|
||||
% SDX
|
||||
|
||||
U22=0;
|
||||
% determine K1 and K2 observation mapping matrices
|
||||
% This uses the fact that measurements are given by L1*s(t)+L2*x(t)
|
||||
% and s(t) is expressed in the dynamics as
|
||||
% H1*eps(t)+G11*s(t-1)+G12*x(t-1)+G13*x(t).
|
||||
% Thus the observations o(t) can be written in the form
|
||||
% o(t)=K1*[eps(t)' s(t-1)' x(t-1)']' + K2*x(t) where
|
||||
% K1=[L1*H1 L1*G11 L1*G12] K2=L1*G13+L2
|
||||
|
||||
G12=G1(NX+1:ss-2*FL_RANK,:);
|
||||
KK1=L1*G12;
|
||||
K1=KK1(:,1:ss-FL_RANK);
|
||||
K2=KK1(:,ss-FL_RANK+1:ss)+L2;
|
||||
|
||||
%pre calculate time-invariant factors
|
||||
A11=G1(1:pd,1:pd);
|
||||
A22=G1(pd+1:end, pd+1:end);
|
||||
A12=G1(1:pd, pd+1:end);
|
||||
A21=G1(pd+1:end,1:pd);
|
||||
Lambda= nmat*A12+A22;
|
||||
%A11_A12Nmat= A11-A12*nmat % test
|
||||
I_L=inv(Lambda);
|
||||
BB=A12*inv(A22);
|
||||
FF=K2*inv(A22);
|
||||
QQ=BB*U22*BB' + U11;
|
||||
UFT=U22*FF';
|
||||
% kf_param structure:
|
||||
AA=A11-BB*A21;
|
||||
CCCC=A11-A12*nmat; % F in new notation
|
||||
DD=K1-FF*A21; % H in new notation
|
||||
EE=K1-K2*nmat;
|
||||
RR=FF*UFT+VV;
|
||||
if ~any(RR)
|
||||
% if zero add some dummy measurement err. variance-covariances
|
||||
% with diagonals 0.000001. This would not be needed if we used
|
||||
% the slow solver, or the generalised eigenvalue approach,
|
||||
% but these are both slower.
|
||||
RR=eye(size(RR,1))*1.0e-6;
|
||||
end
|
||||
SS=BB*UFT;
|
||||
VKLUFT=VV+K2*I_L*UFT;
|
||||
ALUFT=A12*I_L*UFT;
|
||||
FULKV=FF*U22*I_L'*K2'+VV;
|
||||
FUBT=FF*U22*BB';
|
||||
nmat=nmat;
|
||||
% initialise pshat
|
||||
AQDS=AA*QQ*DD'+SS;
|
||||
DQDR=DD*QQ*DD'+RR;
|
||||
I_DQDR=inv(DQDR);
|
||||
AQDQ=AQDS*I_DQDR;
|
||||
ff=AA-AQDQ*DD;
|
||||
hh=AA*QQ*AA'-AQDQ*AQDS';%*(DD*QQ*AA'+SS');
|
||||
rr=DD*QQ*DD'+RR;
|
||||
ZSIG0=disc_riccati_fast(ff,DD,rr,hh);
|
||||
PP=ZSIG0 +QQ;
|
||||
|
||||
exo_names=M_.exo_names(M_.exo_names_orig_ord,:);
|
||||
|
||||
DPDR=DD*PP*DD'+RR;
|
||||
I_DPDR=inv(DPDR);
|
||||
%GG=[ CCCC, zeros(pd,NETA); -nmat*CCCC, zeros(NETA,NETA)];
|
||||
PDIDPDRD=PP*DD'*I_DPDR*DD;
|
||||
MSIG=disclyap_fast(CCCC, CCCC*PDIDPDRD*PP*CCCC');
|
||||
|
||||
COV_P=[ PP, PP; PP, PP+MSIG]; % P0
|
||||
|
||||
dr.PI_GG=[CCCC (AA-CCCC)*(eye(ss-FL_RANK)-PDIDPDRD); zeros(ss-FL_RANK) AA*(eye(ss-FL_RANK)-PDIDPDRD)];
|
||||
|
||||
GAM= [ AA*(eye(ss-FL_RANK)-PDIDPDRD) zeros(ss-FL_RANK); (AA-CCCC)*(eye(ss-FL_RANK)-PDIDPDRD), CCCC];
|
||||
|
||||
VV = [ dr.PI_TT1 dr.PI_TT2];
|
||||
nn=size(VV,1);
|
||||
COV_OMEGA= COV_P( end-nn+1:end, end-nn+1:end);
|
||||
COV_YR0= VV*COV_OMEGA*VV';
|
||||
diagCovYR0=diag(COV_YR0);
|
||||
labels = deblank(M_.endo_names(ivar,:));
|
||||
|
||||
if options_.nomoments == 0
|
||||
z = [ sqrt(diagCovYR0(ivar)) diagCovYR0(ivar) ];
|
||||
title='MOMENTS OF SIMULATED VARIABLES';
|
||||
headers=strvcat('VARIABLE','STD. DEV.','VARIANCE');
|
||||
dyntable(title,headers,labels,z,size(labels,2)+2,16,10);
|
||||
end
|
||||
if options_.nocorr == 0
|
||||
diagSqrtCovYR0=sqrt(diagCovYR0);
|
||||
%COR_Y= diag(diagSqrtCovYR0)*COV_YR0*diag(diagSqrtCovYR0);
|
||||
DELTA=inv(diag(diagSqrtCovYR0));
|
||||
COR_Y= DELTA*COV_YR0*DELTA;
|
||||
title = 'CORRELATION OF SIMULATED VARIABLES';
|
||||
headers = strvcat('VARIABLE',M_.endo_names(ivar,:));
|
||||
dyntable(title,headers,labels,COR_Y(ivar,ivar),size(labels,2)+2,8,4);
|
||||
else
|
||||
COR_Y=[];
|
||||
end
|
||||
|
||||
ar = options_.ar;
|
||||
options_ = set_default_option(options_,'ar',5);
|
||||
ar = options_.ar;
|
||||
if ar > 0
|
||||
COV_YRk= zeros(nn,ar);
|
||||
AutoCOR_YRk= zeros(nn,ar);
|
||||
for k=1:ar;
|
||||
COV_P=GAM*COV_P;
|
||||
COV_OMEGA= COV_P( end-nn+1:end, end-nn+1:end);
|
||||
COV_YRk = VV*COV_OMEGA*VV';
|
||||
AutoCOR_YRkMAT=DELTA*COV_YRk*DELTA;
|
||||
oo_.autocorr{k}=AutoCOR_YRkMAT(ivar,ivar);
|
||||
AutoCOR_YRk(:,k)= diag(COV_YRk)./diagCovYR0;
|
||||
end
|
||||
title = 'AUTOCORRELATION OF SIMULATED VARIABLES';
|
||||
headers = strvcat('VARIABLE',int2str([1:ar]'));
|
||||
dyntable(title,headers,labels,AutoCOR_YRk(ivar,:),size(labels,2)+2,8,4);
|
||||
else
|
||||
AutoCOR_YRk=[];
|
||||
end
|
||||
save ([M_.fname '_PCL_moments'], 'COV_YR0','AutoCOR_YRk', 'COR_Y');
|
||||
warning(warning_old_state);
|
||||
function [irfmat,irfst]=PCL_Part_info_moments( H, varobs, dr,ivar)
|
||||
% sets up parameters and calls part-info kalman filter
|
||||
% developed by G Perendia, July 2006 for implementation from notes by Prof. Joe Pearlman to
|
||||
% suit partial information RE solution in accordance with, and based on, the
|
||||
% Pearlman, Currie and Levine 1986 solution.
|
||||
% 22/10/06 - Version 2 for new Riccati with 4 params instead 5
|
||||
|
||||
% Copyright (C) 2001-20010 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
% Recall that the state space is given by the
|
||||
% predetermined variables s(t-1), x(t-1)
|
||||
% and the jump variables x(t).
|
||||
% The jump variables have dimension NETA
|
||||
|
||||
global M_ options_ oo_
|
||||
warning_old_state = warning;
|
||||
warning off
|
||||
|
||||
[junk,OBS] = ismember(varobs,M_.endo_names,'rows');
|
||||
|
||||
G1=dr.PI_ghx;
|
||||
impact=dr.PI_ghu;
|
||||
nmat=dr.PI_nmat;
|
||||
CC=dr.PI_CC;
|
||||
NX=M_.exo_nbr; % no of exogenous varexo shock variables.
|
||||
% NETA=dr.nfwrd+dr.nboth; % total no of exp. errors set to no of forward looking equations
|
||||
FL_RANK=dr.PI_FL_RANK;
|
||||
NY=M_.endo_nbr;
|
||||
if isempty(OBS)
|
||||
NOBS=NY;
|
||||
LL=eye(NY,NY);
|
||||
else %and if no obsevations specify OBS=[0] but this is not going to work properly
|
||||
NOBS=length(OBS);
|
||||
LL=zeros(NOBS,NY);
|
||||
for i=1:NOBS
|
||||
LL(i,OBS(i))=1;
|
||||
end
|
||||
end
|
||||
|
||||
if exist( 'irfpers')==1
|
||||
if ~isempty(irfpers)
|
||||
if irfpers<=0, irfpers=20, end;
|
||||
else
|
||||
irfpers=20;
|
||||
end
|
||||
else
|
||||
irfpers=20;
|
||||
end
|
||||
|
||||
ss=size(G1,1);
|
||||
|
||||
pd=ss-size(nmat,1);
|
||||
SDX=M_.Sigma_e^0.5; % =SD,not V-COV, of Exog shocks or M_.Sigma_e^0.5 num_exog x num_exog matrix
|
||||
if isempty(H)
|
||||
H=M_.H;
|
||||
end
|
||||
VV=H; % V-COV of observation errors.
|
||||
MM=impact*SDX; % R*(Q^0.5) in standard KF notation
|
||||
% observation vector indices
|
||||
% mapping to endogenous variables.
|
||||
|
||||
L1=LL*dr.PI_TT1;
|
||||
L2=LL*dr.PI_TT2;
|
||||
|
||||
MM1=MM(1:ss-FL_RANK,:);
|
||||
U11=MM1*MM1';
|
||||
% SDX
|
||||
|
||||
U22=0;
|
||||
% determine K1 and K2 observation mapping matrices
|
||||
% This uses the fact that measurements are given by L1*s(t)+L2*x(t)
|
||||
% and s(t) is expressed in the dynamics as
|
||||
% H1*eps(t)+G11*s(t-1)+G12*x(t-1)+G13*x(t).
|
||||
% Thus the observations o(t) can be written in the form
|
||||
% o(t)=K1*[eps(t)' s(t-1)' x(t-1)']' + K2*x(t) where
|
||||
% K1=[L1*H1 L1*G11 L1*G12] K2=L1*G13+L2
|
||||
|
||||
G12=G1(NX+1:ss-2*FL_RANK,:);
|
||||
KK1=L1*G12;
|
||||
K1=KK1(:,1:ss-FL_RANK);
|
||||
K2=KK1(:,ss-FL_RANK+1:ss)+L2;
|
||||
|
||||
%pre calculate time-invariant factors
|
||||
A11=G1(1:pd,1:pd);
|
||||
A22=G1(pd+1:end, pd+1:end);
|
||||
A12=G1(1:pd, pd+1:end);
|
||||
A21=G1(pd+1:end,1:pd);
|
||||
Lambda= nmat*A12+A22;
|
||||
%A11_A12Nmat= A11-A12*nmat % test
|
||||
I_L=inv(Lambda);
|
||||
BB=A12*inv(A22);
|
||||
FF=K2*inv(A22);
|
||||
QQ=BB*U22*BB' + U11;
|
||||
UFT=U22*FF';
|
||||
% kf_param structure:
|
||||
AA=A11-BB*A21;
|
||||
CCCC=A11-A12*nmat; % F in new notation
|
||||
DD=K1-FF*A21; % H in new notation
|
||||
EE=K1-K2*nmat;
|
||||
RR=FF*UFT+VV;
|
||||
if ~any(RR)
|
||||
% if zero add some dummy measurement err. variance-covariances
|
||||
% with diagonals 0.000001. This would not be needed if we used
|
||||
% the slow solver, or the generalised eigenvalue approach,
|
||||
% but these are both slower.
|
||||
RR=eye(size(RR,1))*1.0e-6;
|
||||
end
|
||||
SS=BB*UFT;
|
||||
VKLUFT=VV+K2*I_L*UFT;
|
||||
ALUFT=A12*I_L*UFT;
|
||||
FULKV=FF*U22*I_L'*K2'+VV;
|
||||
FUBT=FF*U22*BB';
|
||||
nmat=nmat;
|
||||
% initialise pshat
|
||||
AQDS=AA*QQ*DD'+SS;
|
||||
DQDR=DD*QQ*DD'+RR;
|
||||
I_DQDR=inv(DQDR);
|
||||
AQDQ=AQDS*I_DQDR;
|
||||
ff=AA-AQDQ*DD;
|
||||
hh=AA*QQ*AA'-AQDQ*AQDS';%*(DD*QQ*AA'+SS');
|
||||
rr=DD*QQ*DD'+RR;
|
||||
ZSIG0=disc_riccati_fast(ff,DD,rr,hh);
|
||||
PP=ZSIG0 +QQ;
|
||||
|
||||
exo_names=M_.exo_names(M_.exo_names_orig_ord,:);
|
||||
|
||||
DPDR=DD*PP*DD'+RR;
|
||||
I_DPDR=inv(DPDR);
|
||||
%GG=[ CCCC, zeros(pd,NETA); -nmat*CCCC, zeros(NETA,NETA)];
|
||||
PDIDPDRD=PP*DD'*I_DPDR*DD;
|
||||
MSIG=disclyap_fast(CCCC, CCCC*PDIDPDRD*PP*CCCC');
|
||||
|
||||
COV_P=[ PP, PP; PP, PP+MSIG]; % P0
|
||||
|
||||
dr.PI_GG=[CCCC (AA-CCCC)*(eye(ss-FL_RANK)-PDIDPDRD); zeros(ss-FL_RANK) AA*(eye(ss-FL_RANK)-PDIDPDRD)];
|
||||
|
||||
GAM= [ AA*(eye(ss-FL_RANK)-PDIDPDRD) zeros(ss-FL_RANK); (AA-CCCC)*(eye(ss-FL_RANK)-PDIDPDRD), CCCC];
|
||||
|
||||
VV = [ dr.PI_TT1 dr.PI_TT2];
|
||||
nn=size(VV,1);
|
||||
COV_OMEGA= COV_P( end-nn+1:end, end-nn+1:end);
|
||||
COV_YR0= VV*COV_OMEGA*VV';
|
||||
diagCovYR0=diag(COV_YR0);
|
||||
labels = deblank(M_.endo_names(ivar,:));
|
||||
|
||||
if options_.nomoments == 0
|
||||
z = [ sqrt(diagCovYR0(ivar)) diagCovYR0(ivar) ];
|
||||
title='MOMENTS OF SIMULATED VARIABLES';
|
||||
headers=strvcat('VARIABLE','STD. DEV.','VARIANCE');
|
||||
dyntable(title,headers,labels,z,size(labels,2)+2,16,10);
|
||||
end
|
||||
if options_.nocorr == 0
|
||||
diagSqrtCovYR0=sqrt(diagCovYR0);
|
||||
%COR_Y= diag(diagSqrtCovYR0)*COV_YR0*diag(diagSqrtCovYR0);
|
||||
DELTA=inv(diag(diagSqrtCovYR0));
|
||||
COR_Y= DELTA*COV_YR0*DELTA;
|
||||
title = 'CORRELATION OF SIMULATED VARIABLES';
|
||||
headers = strvcat('VARIABLE',M_.endo_names(ivar,:));
|
||||
dyntable(title,headers,labels,COR_Y(ivar,ivar),size(labels,2)+2,8,4);
|
||||
else
|
||||
COR_Y=[];
|
||||
end
|
||||
|
||||
ar = options_.ar;
|
||||
options_ = set_default_option(options_,'ar',5);
|
||||
ar = options_.ar;
|
||||
if ar > 0
|
||||
COV_YRk= zeros(nn,ar);
|
||||
AutoCOR_YRk= zeros(nn,ar);
|
||||
for k=1:ar;
|
||||
COV_P=GAM*COV_P;
|
||||
COV_OMEGA= COV_P( end-nn+1:end, end-nn+1:end);
|
||||
COV_YRk = VV*COV_OMEGA*VV';
|
||||
AutoCOR_YRkMAT=DELTA*COV_YRk*DELTA;
|
||||
oo_.autocorr{k}=AutoCOR_YRkMAT(ivar,ivar);
|
||||
AutoCOR_YRk(:,k)= diag(COV_YRk)./diagCovYR0;
|
||||
end
|
||||
title = 'AUTOCORRELATION OF SIMULATED VARIABLES';
|
||||
headers = strvcat('VARIABLE',int2str([1:ar]'));
|
||||
dyntable(title,headers,labels,AutoCOR_YRk(ivar,:),size(labels,2)+2,8,4);
|
||||
else
|
||||
AutoCOR_YRk=[];
|
||||
end
|
||||
save ([M_.fname '_PCL_moments'], 'COV_YR0','AutoCOR_YRk', 'COR_Y');
|
||||
warning(warning_old_state);
|
||||
|
|
|
@ -1,244 +1,244 @@
|
|||
function [G1pi,C,impact,nmat,TT1,TT2,gev,eu, DD, E3, E5]=PI_gensys(a0,a1,a2,a3,c,PSI,NX,NETA,FL_RANK,M_,options_)
|
||||
% System given as
|
||||
% a0*E_t[y(t+1])+a1*y(t)=a2*y(t-1)+c+psi*eps(t)
|
||||
% with z an exogenous variable process.
|
||||
% Returned system is
|
||||
% [s(t)' x(t)' E_t x(t+1)']'=G1pi [s(t-1)' x(t-1)' x(t)]'+C+impact*eps(t),
|
||||
% and (a) the matrix nmat satisfying nmat*E_t z(t)+ E_t x(t+1)=0
|
||||
% (b) matrices TT1, TT2 that relate y(t) to these states: y(t)=[TT1 TT2][s(t)' x(t)']'.
|
||||
% Note that the dimension of the state vector = dim(a0)+NO_FL_EQS
|
||||
%
|
||||
% If div is omitted from argument list, a div>1 is calculated.
|
||||
% eu(1)=1 for existence, eu(2)=1 for uniqueness. eu(1)=-1 for
|
||||
% existence only with not-s.c. z; eu=[-2,-2] for coincident zeros.
|
||||
% Based on
|
||||
% Christopher A. Sims
|
||||
% Corrected 10/28/96 by CAS
|
||||
|
||||
% Copyright (C) 1996-2010 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
|
||||
global lq_instruments;
|
||||
eu=[0;0];C=c;
|
||||
realsmall=1e-6;
|
||||
fixdiv=(nargin==6);
|
||||
n=size(a0,1);
|
||||
DD=[];E3=[]; E5=0;%[];
|
||||
%
|
||||
% Find SVD of a0, and create partitions of U, S and V
|
||||
%
|
||||
[U0,S0,V0] = svd(a0);
|
||||
|
||||
FL_RANK=rank(S0);
|
||||
U01=U0(1:n,1:FL_RANK);
|
||||
U02=U0(1:n,FL_RANK+1:n);
|
||||
V01=V0(1:n,1:FL_RANK);
|
||||
V02=V0(1:n,FL_RANK+1:n);
|
||||
S01=S0(1:FL_RANK,1:FL_RANK);
|
||||
%
|
||||
% Define TT1, TT2
|
||||
%
|
||||
TT1=V02;
|
||||
TT2=V01;
|
||||
%
|
||||
%Invert S01
|
||||
%
|
||||
Sinv=eye(FL_RANK);
|
||||
for i=1:FL_RANK
|
||||
Sinv(i,i)=1/S01(i,i);
|
||||
end
|
||||
%
|
||||
%Set up the system matrix for the variables s(t)=V02*Y(t), x(t)=V01*Y(t) and E_t x(t+1)
|
||||
% and define z(t)'=[s(t)' x(t)]
|
||||
%
|
||||
FF=Sinv*U01'*a1*V02;
|
||||
UAVinv=inv(U02'*a1*V02);
|
||||
G11=UAVinv*U02'*a2*V02;
|
||||
G12=UAVinv*U02'*a2*V01;
|
||||
G13=-UAVinv*U02'*a1*V01;
|
||||
G31=-FF*G11+Sinv*U01'*a2*V02;
|
||||
G32=-FF*G12+Sinv*U01'*a2*V01;
|
||||
G33=-FF*G13-Sinv*U01'*a1*V01;
|
||||
H1=UAVinv*U02'*PSI;
|
||||
H3=-FF*H1+Sinv*U01'*PSI; % This should equal 0
|
||||
G21=zeros(FL_RANK,n-FL_RANK);
|
||||
G22=zeros(FL_RANK,FL_RANK);
|
||||
G23=eye(FL_RANK);
|
||||
%H2=zeros(FL_RANK,NX);
|
||||
num_inst=0;
|
||||
P1=H1;
|
||||
P3=H3;
|
||||
if(options_.ACES_solver==1 & isfield(lq_instruments,'names'))
|
||||
num_inst=size(lq_instruments.names,1);
|
||||
if ~isfield(lq_instruments,'inst_varexo_indices') & num_inst>0
|
||||
for i=1:num_inst
|
||||
i_tmp = strmatch(deblank(lq_instruments.names(i,:)),M_.exo_names,'exact');
|
||||
if isempty(i_tmp)
|
||||
error (['One of the specified instrument variables does not exist']) ;
|
||||
else
|
||||
i_var(i) = i_tmp;
|
||||
end
|
||||
end
|
||||
lq_instruments.inst_varexo_indices=i_var;
|
||||
elseif size(lq_instruments.inst_varexo_indices)>0
|
||||
i_var=lq_instruments.inst_varexo_indices;
|
||||
if ~num_inst
|
||||
num_inst=size(lq_instruments.inst_varexo_indices);
|
||||
end
|
||||
else
|
||||
i_var=[];
|
||||
num_inst=0;
|
||||
end
|
||||
if size(i_var,2)>0 & size(i_var,2)==num_inst
|
||||
N1=H1(:,i_var);
|
||||
N3=H3(:,i_var);
|
||||
x_var=zeros(NX,1);
|
||||
for i=1:NX
|
||||
if isempty(find(i_var==i))
|
||||
x_var(i)=i;
|
||||
end
|
||||
end
|
||||
x_var=nonzeros(x_var);
|
||||
P1=H1(:,x_var);
|
||||
P3=H3(:,x_var);
|
||||
NX=NX-num_inst;
|
||||
else
|
||||
error('WARNING: There are no instrumnets for ACES!');
|
||||
end
|
||||
lq_instruments.N1=N1;
|
||||
lq_instruments.N3=N3;
|
||||
elseif(options_.ACES_solver==1)
|
||||
error('WARNING: There are no instrumnets for ACES!');
|
||||
end
|
||||
% New Definitions
|
||||
Ze11=zeros(NX,NX);
|
||||
Ze12=zeros(NX,n-FL_RANK);
|
||||
Ze134=zeros(NX,FL_RANK);
|
||||
Ze31=zeros(FL_RANK,NX);
|
||||
|
||||
% End of New Definitions
|
||||
|
||||
%
|
||||
% System matrix is called 'G1pi'; Shock matrix is called 'impact'
|
||||
%
|
||||
|
||||
G1pi=[Ze11 Ze12 Ze134 Ze134; P1 G11 G12 G13; Ze31 G21 G22 G23; P3 G31 G32 G33];
|
||||
|
||||
impact=[eye(NX,NX); zeros(n+FL_RANK,NX)];
|
||||
|
||||
if(options_.ACES_solver==1)
|
||||
E3=V02*[P1 G11 G12 G13];
|
||||
E3=E3+ [zeros(size(V01,1),size(E3,2)-size(V01,2)) V01];
|
||||
E5=-V02*N1;
|
||||
DD=[zeros(NX,size(N1,2));N1; zeros(FL_RANK,size(N1,2));N3];
|
||||
eu =[1; 1], nmat=[], gev=[];
|
||||
return; % do not check B&K compliancy
|
||||
end
|
||||
|
||||
G0pi=eye(n+FL_RANK+NX);
|
||||
try
|
||||
[a b q z v]=qz(G0pi,G1pi);
|
||||
catch
|
||||
try
|
||||
lerror=lasterror;
|
||||
disp(['PI_Gensys: ' lerror.message]);
|
||||
if 0==strcmp('MATLAB:qz:matrixWithNaNInf',lerror.identifier)
|
||||
disp '** Unexpected Error PI_Gensys:qz: ** :';
|
||||
button=questdlg('Continue Y/N?','Unexpected Error in qz','No','Yes','Yes');
|
||||
switch button
|
||||
case 'No'
|
||||
error ('Terminated')
|
||||
%case 'Yes'
|
||||
|
||||
end
|
||||
end
|
||||
G1pi=[];impact=[];nmat=[]; gev=[];
|
||||
eu=[-2;-2];
|
||||
return
|
||||
catch
|
||||
disp '** Unexpected Error in qz ** :';
|
||||
disp lerror.message;
|
||||
button=questdlg('Continue Y/N?','Unexpected Error in qz','No','Yes','Yes');
|
||||
switch button
|
||||
case 'No'
|
||||
error ('Terminated')
|
||||
case 'Yes'
|
||||
G1pi=[];impact=[];nmat=[]; gev=[];
|
||||
eu=[-2;-2];
|
||||
return
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
if ~fixdiv, div=1.01; end
|
||||
nunstab=0;
|
||||
zxz=0;
|
||||
nn=size(a,1);
|
||||
for i=1:nn
|
||||
% ------------------div calc------------
|
||||
if ~fixdiv
|
||||
if abs(a(i,i)) > 0
|
||||
divhat=abs(b(i,i))/abs(a(i,i));
|
||||
% bug detected by Vasco Curdia and Daria Finocchiaro, 2/25/2004 A root of
|
||||
% exactly 1.01 and no root between 1 and 1.02, led to div being stuck at 1.01
|
||||
% and the 1.01 root being misclassified as stable. Changing < to <= below fixes this.
|
||||
if 1+realsmall<divhat & divhat<=div
|
||||
div=.5*(1+divhat);
|
||||
end
|
||||
end
|
||||
end
|
||||
% ----------------------------------------
|
||||
nunstab=nunstab+(abs(b(i,i))>div*abs(a(i,i)));
|
||||
if abs(a(i,i))<realsmall & abs(b(i,i))<realsmall
|
||||
zxz=1;
|
||||
end
|
||||
end
|
||||
div ;
|
||||
if ~zxz
|
||||
[a b q z]=qzdiv(div,a,b,q,z);
|
||||
end
|
||||
|
||||
gev=[diag(a) diag(b)];
|
||||
if zxz
|
||||
disp('Coincident zeros. Indeterminacy and/or nonexistence.')
|
||||
eu=[-2;-2];
|
||||
% correction added 7/29/2003. Otherwise the failure to set output
|
||||
% arguments leads to an error message and no output (including eu).
|
||||
nmat=[]; %;gev=[]
|
||||
return
|
||||
end
|
||||
if (FL_RANK ~= nunstab & options_.ACES_solver~=1)
|
||||
disp(['Number of unstable variables ' num2str(nunstab)]);
|
||||
disp( ['does not match number of expectational equations ' num2str(FL_RANK)]);
|
||||
nmat=[];% gev=[];
|
||||
eu=[-2;-2];
|
||||
return
|
||||
end
|
||||
|
||||
% New Definitions
|
||||
z1=z(:,1:n+NX)';
|
||||
z2=z(:,n+NX+1:n+NX+FL_RANK)';
|
||||
|
||||
% New N Matrix by J Pearlman
|
||||
z12=z2(:,1:n+NX);
|
||||
z22=z2(:,n+NX+1:n+NX+FL_RANK);
|
||||
% End of New Definitions
|
||||
|
||||
% modified by GP:
|
||||
nmat=real(inv(z22)*z12);
|
||||
eu=[1;1];
|
||||
function [G1pi,C,impact,nmat,TT1,TT2,gev,eu, DD, E3, E5]=PI_gensys(a0,a1,a2,a3,c,PSI,NX,NETA,FL_RANK,M_,options_)
|
||||
% System given as
|
||||
% a0*E_t[y(t+1])+a1*y(t)=a2*y(t-1)+c+psi*eps(t)
|
||||
% with z an exogenous variable process.
|
||||
% Returned system is
|
||||
% [s(t)' x(t)' E_t x(t+1)']'=G1pi [s(t-1)' x(t-1)' x(t)]'+C+impact*eps(t),
|
||||
% and (a) the matrix nmat satisfying nmat*E_t z(t)+ E_t x(t+1)=0
|
||||
% (b) matrices TT1, TT2 that relate y(t) to these states: y(t)=[TT1 TT2][s(t)' x(t)']'.
|
||||
% Note that the dimension of the state vector = dim(a0)+NO_FL_EQS
|
||||
%
|
||||
% If div is omitted from argument list, a div>1 is calculated.
|
||||
% eu(1)=1 for existence, eu(2)=1 for uniqueness. eu(1)=-1 for
|
||||
% existence only with not-s.c. z; eu=[-2,-2] for coincident zeros.
|
||||
% Based on
|
||||
% Christopher A. Sims
|
||||
% Corrected 10/28/96 by CAS
|
||||
|
||||
% Copyright (C) 1996-2010 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
|
||||
global lq_instruments;
|
||||
eu=[0;0];C=c;
|
||||
realsmall=1e-6;
|
||||
fixdiv=(nargin==6);
|
||||
n=size(a0,1);
|
||||
DD=[];E3=[]; E5=0;%[];
|
||||
%
|
||||
% Find SVD of a0, and create partitions of U, S and V
|
||||
%
|
||||
[U0,S0,V0] = svd(a0);
|
||||
|
||||
FL_RANK=rank(S0);
|
||||
U01=U0(1:n,1:FL_RANK);
|
||||
U02=U0(1:n,FL_RANK+1:n);
|
||||
V01=V0(1:n,1:FL_RANK);
|
||||
V02=V0(1:n,FL_RANK+1:n);
|
||||
S01=S0(1:FL_RANK,1:FL_RANK);
|
||||
%
|
||||
% Define TT1, TT2
|
||||
%
|
||||
TT1=V02;
|
||||
TT2=V01;
|
||||
%
|
||||
%Invert S01
|
||||
%
|
||||
Sinv=eye(FL_RANK);
|
||||
for i=1:FL_RANK
|
||||
Sinv(i,i)=1/S01(i,i);
|
||||
end
|
||||
%
|
||||
%Set up the system matrix for the variables s(t)=V02*Y(t), x(t)=V01*Y(t) and E_t x(t+1)
|
||||
% and define z(t)'=[s(t)' x(t)]
|
||||
%
|
||||
FF=Sinv*U01'*a1*V02;
|
||||
UAVinv=inv(U02'*a1*V02);
|
||||
G11=UAVinv*U02'*a2*V02;
|
||||
G12=UAVinv*U02'*a2*V01;
|
||||
G13=-UAVinv*U02'*a1*V01;
|
||||
G31=-FF*G11+Sinv*U01'*a2*V02;
|
||||
G32=-FF*G12+Sinv*U01'*a2*V01;
|
||||
G33=-FF*G13-Sinv*U01'*a1*V01;
|
||||
H1=UAVinv*U02'*PSI;
|
||||
H3=-FF*H1+Sinv*U01'*PSI; % This should equal 0
|
||||
G21=zeros(FL_RANK,n-FL_RANK);
|
||||
G22=zeros(FL_RANK,FL_RANK);
|
||||
G23=eye(FL_RANK);
|
||||
%H2=zeros(FL_RANK,NX);
|
||||
num_inst=0;
|
||||
P1=H1;
|
||||
P3=H3;
|
||||
if(options_.ACES_solver==1 & isfield(lq_instruments,'names'))
|
||||
num_inst=size(lq_instruments.names,1);
|
||||
if ~isfield(lq_instruments,'inst_varexo_indices') & num_inst>0
|
||||
for i=1:num_inst
|
||||
i_tmp = strmatch(deblank(lq_instruments.names(i,:)),M_.exo_names,'exact');
|
||||
if isempty(i_tmp)
|
||||
error (['One of the specified instrument variables does not exist']) ;
|
||||
else
|
||||
i_var(i) = i_tmp;
|
||||
end
|
||||
end
|
||||
lq_instruments.inst_varexo_indices=i_var;
|
||||
elseif size(lq_instruments.inst_varexo_indices)>0
|
||||
i_var=lq_instruments.inst_varexo_indices;
|
||||
if ~num_inst
|
||||
num_inst=size(lq_instruments.inst_varexo_indices);
|
||||
end
|
||||
else
|
||||
i_var=[];
|
||||
num_inst=0;
|
||||
end
|
||||
if size(i_var,2)>0 & size(i_var,2)==num_inst
|
||||
N1=H1(:,i_var);
|
||||
N3=H3(:,i_var);
|
||||
x_var=zeros(NX,1);
|
||||
for i=1:NX
|
||||
if isempty(find(i_var==i))
|
||||
x_var(i)=i;
|
||||
end
|
||||
end
|
||||
x_var=nonzeros(x_var);
|
||||
P1=H1(:,x_var);
|
||||
P3=H3(:,x_var);
|
||||
NX=NX-num_inst;
|
||||
else
|
||||
error('WARNING: There are no instrumnets for ACES!');
|
||||
end
|
||||
lq_instruments.N1=N1;
|
||||
lq_instruments.N3=N3;
|
||||
elseif(options_.ACES_solver==1)
|
||||
error('WARNING: There are no instrumnets for ACES!');
|
||||
end
|
||||
% New Definitions
|
||||
Ze11=zeros(NX,NX);
|
||||
Ze12=zeros(NX,n-FL_RANK);
|
||||
Ze134=zeros(NX,FL_RANK);
|
||||
Ze31=zeros(FL_RANK,NX);
|
||||
|
||||
% End of New Definitions
|
||||
|
||||
%
|
||||
% System matrix is called 'G1pi'; Shock matrix is called 'impact'
|
||||
%
|
||||
|
||||
G1pi=[Ze11 Ze12 Ze134 Ze134; P1 G11 G12 G13; Ze31 G21 G22 G23; P3 G31 G32 G33];
|
||||
|
||||
impact=[eye(NX,NX); zeros(n+FL_RANK,NX)];
|
||||
|
||||
if(options_.ACES_solver==1)
|
||||
E3=V02*[P1 G11 G12 G13];
|
||||
E3=E3+ [zeros(size(V01,1),size(E3,2)-size(V01,2)) V01];
|
||||
E5=-V02*N1;
|
||||
DD=[zeros(NX,size(N1,2));N1; zeros(FL_RANK,size(N1,2));N3];
|
||||
eu =[1; 1], nmat=[], gev=[];
|
||||
return; % do not check B&K compliancy
|
||||
end
|
||||
|
||||
G0pi=eye(n+FL_RANK+NX);
|
||||
try
|
||||
[a b q z v]=qz(G0pi,G1pi);
|
||||
catch
|
||||
try
|
||||
lerror=lasterror;
|
||||
disp(['PI_Gensys: ' lerror.message]);
|
||||
if 0==strcmp('MATLAB:qz:matrixWithNaNInf',lerror.identifier)
|
||||
disp '** Unexpected Error PI_Gensys:qz: ** :';
|
||||
button=questdlg('Continue Y/N?','Unexpected Error in qz','No','Yes','Yes');
|
||||
switch button
|
||||
case 'No'
|
||||
error ('Terminated')
|
||||
%case 'Yes'
|
||||
|
||||
end
|
||||
end
|
||||
G1pi=[];impact=[];nmat=[]; gev=[];
|
||||
eu=[-2;-2];
|
||||
return
|
||||
catch
|
||||
disp '** Unexpected Error in qz ** :';
|
||||
disp lerror.message;
|
||||
button=questdlg('Continue Y/N?','Unexpected Error in qz','No','Yes','Yes');
|
||||
switch button
|
||||
case 'No'
|
||||
error ('Terminated')
|
||||
case 'Yes'
|
||||
G1pi=[];impact=[];nmat=[]; gev=[];
|
||||
eu=[-2;-2];
|
||||
return
|
||||
end
|
||||
end
|
||||
end
|
||||
|
||||
if ~fixdiv, div=1.01; end
|
||||
nunstab=0;
|
||||
zxz=0;
|
||||
nn=size(a,1);
|
||||
for i=1:nn
|
||||
% ------------------div calc------------
|
||||
if ~fixdiv
|
||||
if abs(a(i,i)) > 0
|
||||
divhat=abs(b(i,i))/abs(a(i,i));
|
||||
% bug detected by Vasco Curdia and Daria Finocchiaro, 2/25/2004 A root of
|
||||
% exactly 1.01 and no root between 1 and 1.02, led to div being stuck at 1.01
|
||||
% and the 1.01 root being misclassified as stable. Changing < to <= below fixes this.
|
||||
if 1+realsmall<divhat & divhat<=div
|
||||
div=.5*(1+divhat);
|
||||
end
|
||||
end
|
||||
end
|
||||
% ----------------------------------------
|
||||
nunstab=nunstab+(abs(b(i,i))>div*abs(a(i,i)));
|
||||
if abs(a(i,i))<realsmall & abs(b(i,i))<realsmall
|
||||
zxz=1;
|
||||
end
|
||||
end
|
||||
div ;
|
||||
if ~zxz
|
||||
[a b q z]=qzdiv(div,a,b,q,z);
|
||||
end
|
||||
|
||||
gev=[diag(a) diag(b)];
|
||||
if zxz
|
||||
disp('Coincident zeros. Indeterminacy and/or nonexistence.')
|
||||
eu=[-2;-2];
|
||||
% correction added 7/29/2003. Otherwise the failure to set output
|
||||
% arguments leads to an error message and no output (including eu).
|
||||
nmat=[]; %;gev=[]
|
||||
return
|
||||
end
|
||||
if (FL_RANK ~= nunstab & options_.ACES_solver~=1)
|
||||
disp(['Number of unstable variables ' num2str(nunstab)]);
|
||||
disp( ['does not match number of expectational equations ' num2str(FL_RANK)]);
|
||||
nmat=[];% gev=[];
|
||||
eu=[-2;-2];
|
||||
return
|
||||
end
|
||||
|
||||
% New Definitions
|
||||
z1=z(:,1:n+NX)';
|
||||
z2=z(:,n+NX+1:n+NX+FL_RANK)';
|
||||
|
||||
% New N Matrix by J Pearlman
|
||||
z12=z2(:,1:n+NX);
|
||||
z22=z2(:,n+NX+1:n+NX+FL_RANK);
|
||||
% End of New Definitions
|
||||
|
||||
% modified by GP:
|
||||
nmat=real(inv(z22)*z12);
|
||||
eu=[1;1];
|
||||
|
|
|
@ -1,91 +1,91 @@
|
|||
function Z=disc_riccati_fast(F,D,R,H,ch)
|
||||
% function Z=disc_riccati_fast(F,D,R,H,ch)
|
||||
%
|
||||
% Solves discrete Riccati Equation:
|
||||
% Z=FZF' - FZD'inv(DZD'+R)DZF' + H
|
||||
% Using the Doubling Algorithm
|
||||
%
|
||||
% George Perendia: based on the doubling algorithm for Riccati
|
||||
% and the disclyap_fast function provided by Prof. Joe Pearlman
|
||||
% V.1 19/5/2006
|
||||
% V.2 22/10/06
|
||||
% =================================================================
|
||||
|
||||
% Copyright (C) 1996-2010 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
if nargin == 4 | isempty( ch ) == 1
|
||||
flag_ch = 0;
|
||||
else
|
||||
flag_ch = 1;
|
||||
end
|
||||
|
||||
|
||||
% intialisation
|
||||
tol = 1e-10; % iteration convergence threshold
|
||||
P0=H;
|
||||
X0=F;
|
||||
if ~any(R) % i.e. ==0
|
||||
warning('Dangerously evading inversion of zero matrix!');
|
||||
Y0=zeros(size(R));
|
||||
else
|
||||
Y0=D'*inv(R)*D;
|
||||
end
|
||||
POYO=P0*Y0;
|
||||
I=eye(size(POYO));
|
||||
clear POYO;
|
||||
|
||||
% iterate Riccati equation solution
|
||||
matd=1;
|
||||
count=0;
|
||||
while matd > tol && count < 100
|
||||
INVPY=inv(I+P0*Y0);
|
||||
P1=X0*INVPY*P0*X0'+ P0;
|
||||
Y1=X0'*Y0*INVPY*X0+ Y0;
|
||||
X1=X0*INVPY*X0;
|
||||
matd=sum( sum(abs( P1 - P0 )));
|
||||
% P0=(P1+P1')/2
|
||||
P0=P1;
|
||||
X0=X1;
|
||||
Y0=Y1;
|
||||
count=count+1;
|
||||
% matd;
|
||||
end
|
||||
|
||||
Z=(P0+P0')/2;
|
||||
%Z=P0
|
||||
% check if the convergence took place
|
||||
if count==100
|
||||
matd
|
||||
error('Riccati not converged fast enough!');
|
||||
% error.identifier='Riccati not converged!'
|
||||
% error
|
||||
end
|
||||
%if count >5
|
||||
% disp('Riccati count= ');
|
||||
% count
|
||||
%end
|
||||
|
||||
clear X0 X1 Y0 Y1 P1 I INVPY;
|
||||
|
||||
% Check that X is positive definite
|
||||
if flag_ch==1
|
||||
[C,p]=chol(Z);
|
||||
if p ~= 0
|
||||
error('Z is not positive definite')
|
||||
end
|
||||
end
|
||||
function Z=disc_riccati_fast(F,D,R,H,ch)
|
||||
% function Z=disc_riccati_fast(F,D,R,H,ch)
|
||||
%
|
||||
% Solves discrete Riccati Equation:
|
||||
% Z=FZF' - FZD'inv(DZD'+R)DZF' + H
|
||||
% Using the Doubling Algorithm
|
||||
%
|
||||
% George Perendia: based on the doubling algorithm for Riccati
|
||||
% and the disclyap_fast function provided by Prof. Joe Pearlman
|
||||
% V.1 19/5/2006
|
||||
% V.2 22/10/06
|
||||
% =================================================================
|
||||
|
||||
% Copyright (C) 1996-2010 Dynare Team
|
||||
%
|
||||
% This file is part of Dynare.
|
||||
%
|
||||
% Dynare is free software: you can redistribute it and/or modify
|
||||
% it under the terms of the GNU General Public License as published by
|
||||
% the Free Software Foundation, either version 3 of the License, or
|
||||
% (at your option) any later version.
|
||||
%
|
||||
% Dynare is distributed in the hope that it will be useful,
|
||||
% but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||
% GNU General Public License for more details.
|
||||
%
|
||||
% You should have received a copy of the GNU General Public License
|
||||
% along with Dynare. If not, see <http://www.gnu.org/licenses/>.
|
||||
|
||||
if nargin == 4 | isempty( ch ) == 1
|
||||
flag_ch = 0;
|
||||
else
|
||||
flag_ch = 1;
|
||||
end
|
||||
|
||||
|
||||
% intialisation
|
||||
tol = 1e-10; % iteration convergence threshold
|
||||
P0=H;
|
||||
X0=F;
|
||||
if ~any(R) % i.e. ==0
|
||||
warning('Dangerously evading inversion of zero matrix!');
|
||||
Y0=zeros(size(R));
|
||||
else
|
||||
Y0=D'*inv(R)*D;
|
||||
end
|
||||
POYO=P0*Y0;
|
||||
I=eye(size(POYO));
|
||||
clear POYO;
|
||||
|
||||
% iterate Riccati equation solution
|
||||
matd=1;
|
||||
count=0;
|
||||
while matd > tol && count < 100
|
||||
INVPY=inv(I+P0*Y0);
|
||||
P1=X0*INVPY*P0*X0'+ P0;
|
||||
Y1=X0'*Y0*INVPY*X0+ Y0;
|
||||
X1=X0*INVPY*X0;
|
||||
matd=sum( sum(abs( P1 - P0 )));
|
||||
% P0=(P1+P1')/2
|
||||
P0=P1;
|
||||
X0=X1;
|
||||
Y0=Y1;
|
||||
count=count+1;
|
||||
% matd;
|
||||
end
|
||||
|
||||
Z=(P0+P0')/2;
|
||||
%Z=P0
|
||||
% check if the convergence took place
|
||||
if count==100
|
||||
matd
|
||||
error('Riccati not converged fast enough!');
|
||||
% error.identifier='Riccati not converged!'
|
||||
% error
|
||||
end
|
||||
%if count >5
|
||||
% disp('Riccati count= ');
|
||||
% count
|
||||
%end
|
||||
|
||||
clear X0 X1 Y0 Y1 P1 I INVPY;
|
||||
|
||||
% Check that X is positive definite
|
||||
if flag_ch==1
|
||||
[C,p]=chol(Z);
|
||||
if p ~= 0
|
||||
error('Z is not positive definite')
|
||||
end
|
||||
end
|
||||
|
|
|
@ -1,47 +1,47 @@
|
|||
% inflation target (pitarg) modelled as AR1
|
||||
% cy = 0.614479/0.769365 - obtained from the computed steady state from the nonlinear counterpart
|
||||
|
||||
|
||||
var pi mc mun muc c y n r g a pitarg ;
|
||||
varexo eps_g eps_a eps_e eps_m eps_targ;
|
||||
|
||||
parameters beta xi hc wd sigma gamma rho_g rho_a rho_r rho_targ thetap cy varrho;
|
||||
beta = 0.99;
|
||||
xi = 0.5034;
|
||||
hc = 0.0;
|
||||
wd = 0.40;
|
||||
gamma = 0.5868;
|
||||
sigma = 4.0897;
|
||||
cy = 0.614479/0.769365;
|
||||
rho_g = 0.8325;
|
||||
rho_a = 0.9827;
|
||||
rho_r = 0.3529;
|
||||
thetap = 2.2161;
|
||||
varrho = 0.3853;
|
||||
rho_targ = 0.6133;
|
||||
|
||||
model(linear);
|
||||
pi = (beta/(1+beta*gamma))*pi(+1)+(gamma/(1+beta*gamma))*pi(-1)+(((1-beta*xi)*(1-xi))/((1+beta*gamma)*xi))*(mc+eps_m);
|
||||
mc = mun-muc-a;
|
||||
mun = (c-hc*c(-1))/(1-hc)+wd*n/(1-wd)+muc;
|
||||
muc = ((1-varrho)*(1-sigma)-1)*(c-hc*c(-1))/(1-hc)-wd*varrho*(1-sigma)*n/(1-wd);
|
||||
muc(+1) = muc-(r-pi(+1));
|
||||
y = cy*c+(1-cy)*g;
|
||||
n = y-a;
|
||||
r = rho_r*r(-1)+thetap*(1-rho_r)*(pi(+1)-rho_targ*pitarg)+eps_e;
|
||||
g = rho_g*g(-1)+eps_g;
|
||||
a = rho_a*a(-1)+eps_a;
|
||||
pitarg = rho_targ*pitarg(-1)+eps_targ;
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var eps_g; stderr 3.8505;
|
||||
var eps_a; stderr 0.7573;
|
||||
var eps_e; stderr 0.2409;
|
||||
var eps_m; stderr 0.8329;
|
||||
var eps_targ; stderr 0.3978;
|
||||
end;
|
||||
|
||||
varobs pi mc mun muc c y n r g a pitarg;
|
||||
stoch_simul(partial_information,irf=30)pi y r;//pi n c y r;
|
||||
|
||||
% inflation target (pitarg) modelled as AR1
|
||||
% cy = 0.614479/0.769365 - obtained from the computed steady state from the nonlinear counterpart
|
||||
|
||||
|
||||
var pi mc mun muc c y n r g a pitarg ;
|
||||
varexo eps_g eps_a eps_e eps_m eps_targ;
|
||||
|
||||
parameters beta xi hc wd sigma gamma rho_g rho_a rho_r rho_targ thetap cy varrho;
|
||||
beta = 0.99;
|
||||
xi = 0.5034;
|
||||
hc = 0.0;
|
||||
wd = 0.40;
|
||||
gamma = 0.5868;
|
||||
sigma = 4.0897;
|
||||
cy = 0.614479/0.769365;
|
||||
rho_g = 0.8325;
|
||||
rho_a = 0.9827;
|
||||
rho_r = 0.3529;
|
||||
thetap = 2.2161;
|
||||
varrho = 0.3853;
|
||||
rho_targ = 0.6133;
|
||||
|
||||
model(linear);
|
||||
pi = (beta/(1+beta*gamma))*pi(+1)+(gamma/(1+beta*gamma))*pi(-1)+(((1-beta*xi)*(1-xi))/((1+beta*gamma)*xi))*(mc+eps_m);
|
||||
mc = mun-muc-a;
|
||||
mun = (c-hc*c(-1))/(1-hc)+wd*n/(1-wd)+muc;
|
||||
muc = ((1-varrho)*(1-sigma)-1)*(c-hc*c(-1))/(1-hc)-wd*varrho*(1-sigma)*n/(1-wd);
|
||||
muc(+1) = muc-(r-pi(+1));
|
||||
y = cy*c+(1-cy)*g;
|
||||
n = y-a;
|
||||
r = rho_r*r(-1)+thetap*(1-rho_r)*(pi(+1)-rho_targ*pitarg)+eps_e;
|
||||
g = rho_g*g(-1)+eps_g;
|
||||
a = rho_a*a(-1)+eps_a;
|
||||
pitarg = rho_targ*pitarg(-1)+eps_targ;
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var eps_g; stderr 3.8505;
|
||||
var eps_a; stderr 0.7573;
|
||||
var eps_e; stderr 0.2409;
|
||||
var eps_m; stderr 0.8329;
|
||||
var eps_targ; stderr 0.3978;
|
||||
end;
|
||||
|
||||
varobs pi mc mun muc c y n r g a pitarg;
|
||||
stoch_simul(partial_information,irf=30)pi y r;//pi n c y r;
|
||||
|
||||
|
|
|
@ -1,47 +1,47 @@
|
|||
% inflation target (pitarg) modelled as AR1
|
||||
% cy = 0.614479/0.769365 - obtained from the computed steady state from the nonlinear counterpart
|
||||
|
||||
|
||||
var pi mc mun muc c y n r g a pitarg;
|
||||
varexo eps_g eps_a eps_e eps_m eps_targ;
|
||||
|
||||
parameters beta xi hc wd sigma gamma rho_g rho_a rho_r rho_targ thetap cy varrho;
|
||||
beta = 0.99;
|
||||
xi = 0.5034;
|
||||
hc = 0.0;
|
||||
wd = 0.40;
|
||||
gamma = 0.5868;
|
||||
sigma = 4.0897;
|
||||
cy = 0.614479/0.769365;
|
||||
rho_g = 0.8325;
|
||||
rho_a = 0.9827;
|
||||
rho_r = 0.3529;
|
||||
thetap = 2.2161;
|
||||
varrho = 0.3853;
|
||||
rho_targ = 0.6133;
|
||||
|
||||
model(linear);
|
||||
pi = (beta/(1+beta*gamma))*pi(+1)+(gamma/(1+beta*gamma))*pi(-1)+(((1-beta*xi)*(1-xi))/((1+beta*gamma)*xi))*(mc+eps_m);
|
||||
mc = mun-muc-a;
|
||||
mun = (c-hc*c(-1))/(1-hc)+wd*n/(1-wd)+muc;
|
||||
muc = ((1-varrho)*(1-sigma)-1)*(c-hc*c(-1))/(1-hc)-wd*varrho*(1-sigma)*n/(1-wd);
|
||||
muc(+1) = muc-(r-pi(+1));
|
||||
y = cy*c+(1-cy)*g;
|
||||
n = y-a;
|
||||
r = rho_r*r(-1)+thetap*(1-rho_r)*(pi(+1)-rho_targ*pitarg)+eps_e;
|
||||
g = rho_g*g(-1)+eps_g;
|
||||
a = rho_a*a(-1)+eps_a;
|
||||
pitarg = rho_targ*pitarg(-1)+eps_targ;
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var eps_g; stderr 3.8505;
|
||||
var eps_a; stderr 0.7573;
|
||||
var eps_e; stderr 0.2409;
|
||||
var eps_m; stderr 0.8329;
|
||||
var eps_targ; stderr 0.3978;
|
||||
end;
|
||||
|
||||
varobs c n r ;
|
||||
stoch_simul(partial_information,irf=30)pi y r;//pi n c y r;
|
||||
|
||||
% inflation target (pitarg) modelled as AR1
|
||||
% cy = 0.614479/0.769365 - obtained from the computed steady state from the nonlinear counterpart
|
||||
|
||||
|
||||
var pi mc mun muc c y n r g a pitarg;
|
||||
varexo eps_g eps_a eps_e eps_m eps_targ;
|
||||
|
||||
parameters beta xi hc wd sigma gamma rho_g rho_a rho_r rho_targ thetap cy varrho;
|
||||
beta = 0.99;
|
||||
xi = 0.5034;
|
||||
hc = 0.0;
|
||||
wd = 0.40;
|
||||
gamma = 0.5868;
|
||||
sigma = 4.0897;
|
||||
cy = 0.614479/0.769365;
|
||||
rho_g = 0.8325;
|
||||
rho_a = 0.9827;
|
||||
rho_r = 0.3529;
|
||||
thetap = 2.2161;
|
||||
varrho = 0.3853;
|
||||
rho_targ = 0.6133;
|
||||
|
||||
model(linear);
|
||||
pi = (beta/(1+beta*gamma))*pi(+1)+(gamma/(1+beta*gamma))*pi(-1)+(((1-beta*xi)*(1-xi))/((1+beta*gamma)*xi))*(mc+eps_m);
|
||||
mc = mun-muc-a;
|
||||
mun = (c-hc*c(-1))/(1-hc)+wd*n/(1-wd)+muc;
|
||||
muc = ((1-varrho)*(1-sigma)-1)*(c-hc*c(-1))/(1-hc)-wd*varrho*(1-sigma)*n/(1-wd);
|
||||
muc(+1) = muc-(r-pi(+1));
|
||||
y = cy*c+(1-cy)*g;
|
||||
n = y-a;
|
||||
r = rho_r*r(-1)+thetap*(1-rho_r)*(pi(+1)-rho_targ*pitarg)+eps_e;
|
||||
g = rho_g*g(-1)+eps_g;
|
||||
a = rho_a*a(-1)+eps_a;
|
||||
pitarg = rho_targ*pitarg(-1)+eps_targ;
|
||||
end;
|
||||
|
||||
shocks;
|
||||
var eps_g; stderr 3.8505;
|
||||
var eps_a; stderr 0.7573;
|
||||
var eps_e; stderr 0.2409;
|
||||
var eps_m; stderr 0.8329;
|
||||
var eps_targ; stderr 0.3978;
|
||||
end;
|
||||
|
||||
varobs c n r ;
|
||||
stoch_simul(partial_information,irf=30)pi y r;//pi n c y r;
|
||||
|
||||
|
|
Loading…
Reference in New Issue